

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase			:				List[str]   =     argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="store_true", help="Enable DPMSolver or not")
parser.add_argument("--steps", default=None, type=int, help="Num inference steps")
lowercase			:				Dict   =     parser.parse_args()


lowercase			:				Any   =     "cpu"
lowercase			:				str   =     "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"

lowercase			:				str   =     "path-to-your-trained-model"
lowercase			:				Any   =     StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
		lowercase			:				Tuple   =     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase			:				Optional[int]   =     pipe.to(device)

# to channels last
lowercase			:				str   =     pipe.unet.to(memory_format=torch.channels_last)
lowercase			:				Tuple   =     pipe.vae.to(memory_format=torch.channels_last)
lowercase			:				Tuple   =     pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
		lowercase			:				str   =     pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
lowercase			:				int   =     torch.randn(2, 4, 64, 64)
lowercase			:				str   =     torch.rand(1) * 9_99
lowercase			:				List[str]   =     torch.randn(2, 77, 7_68)
lowercase			:				Optional[Any]   =     (sample, timestep, encoder_hidden_status)
try:
		lowercase			:				Optional[Any]   =     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
		lowercase			:				str   =     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase			:				Dict   =     ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase			:				int   =     ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
		lowercase			:				List[Any]   =     ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase			:				List[Any]   =     6_66
lowercase			:				Tuple   =     torch.Generator(device).manual_seed(seed)
lowercase			:				Union[str, Any]   =     {"generator": generator}
if args.steps is not None:
		lowercase			:				Tuple   =     args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
		lowercase			:				List[Any]   =     pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("generated.png")









import math

import tensorflow as tf
from packaging import version






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[int]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Union[str, Any]      		=					0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0		)     ,				x.dtype		)		))

							return x * cdf






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Any      		=					tf.cast(math.pi     ,				x.dtype		)
							A      :					int      		=					tf.cast(0.044_715     ,				x.dtype		)
							A      :					Tuple      		=					0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi		) * (x + coeff * tf.pow(lowerCamelCase_     ,				3		))		))

							return x * cdf






def        snake_case__		(    lowerCamelCase_		):
							A      :					Union[str, Any]      		=					tf.convert_to_tensor(lowerCamelCase_		)

							return x * tf.tanh(tf.math.softplus(lowerCamelCase_		)		)






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[int]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					List[Any]      		=					tf.cast(0.044_715     ,				x.dtype		)
							A      :					str      		=					tf.cast(0.7_978_845_608     ,				x.dtype		)

							return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)		))






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Any      		=					tf.cast(1.702     ,				x.dtype		)
							return x * tf.math.sigmoid(coeff * x		)






def        snake_case__		(    lowerCamelCase_		):
							return tf.clip_by_value(_gelu(lowerCamelCase_		)     ,				-10     ,				10		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_=-1		):
							A		,		A      :					Dict      		=					tf.split(lowerCamelCase_     ,				2     ,				axis=lowerCamelCase_		)
							return a * tf.math.sigmoid(lowerCamelCase_		)


if version.parse(tf.version.VERSION) >= version.parse("2.4"):






		def        snake_case__		(    lowerCamelCase_		):
									return tf.keras.activations.gelu(lowerCamelCase_     ,				approximate=lowerCamelCase_		)

		lowercase			:				Tuple   =     tf.keras.activations.gelu

		lowercase			:				Union[str, Any]   =     approximate_gelu_wrap
else:
		lowercase			:				str   =     _gelu
		lowercase			:				Dict   =     _gelu_new


lowercase			:				Union[str, Any]   =     {
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}






def        snake_case__		(    lowerCamelCase_		):
							if activation_string in ACTaFN:
														return ACTaFN[activation_string]
							else:
														raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()		)}'		)






